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This paper presents a method for determining the transmission efficiency of a gear reducer. The proposed method contains an identification theory to derive a dynamical model of motors. Using the identification theory, we propose a method to measure the torque transmission efficiency of the gear reducer. The identification theory is derived from the error between input and output data of the mathematical model of a target plant and actual time series data of the plant by an experiment. The dynamic characteristics of the gear reducer are calculated by identification theory for a system installing the motors. The target plant means a motor or a combined system consisting of the motor and the gear reducer. We discuss the torque constant as one parameter that determines motor characteristics. The torque constant value of the motor can be determined using the identification theory. Then, the torque transmission efficiency of the gear reducer can be decided by the torque constant value. In this paper, we first explain the algorithm of the identification theory, second discuss the results of identifying the motor torque constant, and finally examine the transmission efficiency of the gear reducer in the experiment. [ABSTRACT FROM AUTHOR]

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The current multi-objective particle swarm algorithms excel in convergence speed for solving complex problems but often suffer from a loss of population diversity. Conversely, composite differential evolution algorithms maintain superior solution distribution but lag in convergence efficiency. This research introduces an improved hybrid algorithm, CoDE-MOPSO, which integrates multi-objective particle swarm optimization with composite differential evolution based on clustering technology. The clustering algorithm is used for all individual clusters to analyze the distribution constructs of populations, which determines whether the new solutions come from global or local populations at a mating restriction probability. The mating restriction probability is updated at each generation. To adapt the balance between the population solution diversities and the convergence speed of the algorithm, at each generation, the control probability is adjusted by a developed adaptive strategy according to the reproduction utility of the two mechanisms of generating new solutions over the last certain generations. This research introduces the CoDE-MOPSO algorithm, designed to transcend existing multi-objective optimization methods’ limitations by optimally balancing exploration and exploitation. Our approach significantly advances evolutionary multi-objective optimization, demonstrating superior performance through lower Inverse Generational Distance and higher Hypervolume metrics, indicating enhanced efficiency in solving complex MOPs across various fields. In practical scenarios like gear reducer optimization, CoDE-MOPSO showcases remarkable effectiveness, highlighting its value in engineering applications and setting a foundation for sophisticated optimization strategies that combine speed with solution quality.

Selecting appropriate motors and gear reducers for industrial robots considering operational requirements, lifespan, and price is a critical aspect of production automation design. Four-axis palletizing robots are commonly used for material handling in industrial automation, but their complex structure, which includes two parallelogram mechanisms, makes motor and gear reducer selection challenging for these robots. To address this challenge, a practical method for selecting these components based on dynamic analysis using ADAMS software is proposed here. The study considers the dynamic coupling effect between moving robot arms by simulating two conditions: independent motion of each robot axis and simultaneous motion of all robot axes. The analysis provides various torques, such as peak and RMS torque, for each axis, allowing the specification of the motors and reducers to be determined. The feasibility of the proposed method is also evaluated by assessing the suitability of the tentatively selected motor and gear reducer specifications. Therefore, the proposed method can be utilized in the initial design or verification stage of a robot with complex structures. [ABSTRACT FROM AUTHOR]

Aiming at the problems of installation space and signal acquisition of vibration sensors in the actual monitoring of RV reducers, which were prone to limitations and interference, a sparse autoencoder（SAE） and Fisher criterion combination method was proposed for RV gearbox fault feature extraction based on motor current signal analysis（MCSA）. Firstly, the collected drive motor current data were converted to the frequency domain. The effect of different hyperparameters on the feature extraction ability of sparse autoencoder was investigated, and the sparse autoencoder with optimized parameters was used to automatically extract fault features from frequency domain signals. Then the Fisher criterion was used to rank the discriminative ability of the extracted features in descending order, and the top n features in the ranking were taken to obtain the optimal fault feature set. Finally, the SoftMax classification layer was combined to achieve fault diagnosis of RV reducers. The RV reducer fault test bench was built, the motor current data was collected, the method based on Fisher-SAE was verified, and it was compared with other typical machine learning fault diagnosis methods. The research results show that the method can extract fault features from the motor current signal of RV reducer, and select the most effective fault feature set, which solves the limitation of vibration signal and the problem that it is difficult to extract effective features by using current signal for fault diagnosis. Comparing with other typical machine learning fault diagnosis methods, the diagnostic accuracy of this method is increased by 10%～20%, and it has better diagnostic efficiency and accuracy. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy of fault diagnosis result of gear reducer, this paper researches a fault diagnostic new method based on neural network optimized FPN. The BP algorithm has the characteristics of adaptive learning, and the original value of FPN related network parameters is optimized. Based on the FPN network, the BP algorithm is used to train the gear reducer fault samples, so that the FPN parameter values gradually approach the true value. The results of application examples show that this new algorithm for single or multiple fault diagnosis of gear reducer is very effective, the fault diagnosis accuracy has improved significantly, and the effectivity and correctness of optimization algorithm are verified. [ABSTRACT FROM AUTHOR]

Aiming at the reduction gear transmission of a rail conveyor, the two-stage main gear transmission system is taken as an example to establish the contact collision dynamics calculation model of the meshing gear teeth. The contact force of each gear speed and the tooth flanks of a gear with different helix angles in one motion cycle are calculated and analyzed. Based on the kinetic calculation results, the finite element calculation model of gear transmission friction contact is established. The Lagrangian multiplier method is used to calculate and analyze the meshing performance of the teeth under different helix angle contact conditions during one motion cycle. In order to avoid the contact fatigue of the gear teeth, the dangerous position of the tooth surface contact fatigue is obtained by finite element calculation. Based on the miner linear cumulative damage theory, the contact fatigue damage degree of the tooth surface of the tooth is obtained under different helix angle contact conditions. [ABSTRACT FROM AUTHOR]

Highlights • A electromechanical servo-axis model is defined to evaluate axis losses. • A continuous optimization procedure is defined by interpolating discrete catalogs. • The optimal motor-reducer is found minimizing energy consumption, axis size. • Optimization is applied to two case studies, choosing optimal motor-reducer pairs. Abstract This papers deals with the optimal selection of motors and reducers in servo-controlled machines. A model of an electromechanical servo-axis is developed, which allows electrical and mechanical losses to be evaluated. The model is based on data that can be extracted from commercial catalogs, thus requiring no experimental characterization. Then, a novel optimal selection procedure is presented, called continuous optimization, based on the extension of a discrete commercial catalog to a continuous one, by means of data fitting on the electromechanical parameters that influence the motor and reducer operation. A non-linear constrained optimization problem is solved in order to find the optimal motor-reducer unit with respect to a variety of objective functions, such as motor size, overall energetic efficiency, total mass, etc. [ABSTRACT FROM AUTHOR]

Based on the working conditions of the coal mine transport reducer and metallurgical blender reducer,the developments on prototype of the two stages reducer of hypoid- cylindrical gear is carried out. It is presented the scheme of design on the gear reducer,and it is carried on the optimization of the geometric parameter on the hypoid gear. The control of transmission performance on hypoid gear is carried out by the tooth contact analysis. The transmission performance experiments under no- load and several loads on the developed reducer is finished to obtain the data of temperature and noise of the reducer. The results show that the transmission performance of hypoid reducer is excellent,and the reducer has obvious advantages in behalf of realizing a high transmission ratio and the lightweight design of reducer.

Vibration monitoring test of underground belt conveyor gear reducer was conducted by use of vibration recorder. Analysis of changing trend and power spectrum of time-domain characteristic index of on-site vibration data indicates that input shaft of the gear reduer has severe wear and gear tooth breakage.Overhaul results verify correctness of the above analysis and validity of the data sampled by the recorder.

When designing a gear reducer, there are many important factors to be considered, such as weight, size, strength, durability material and geometry. The material of the gear reducer has a key impact on its weight. In this paper, a two-stage gear reducer is optimised with major conflict functions like minimisation of gear material volume, minimisation of centre distance, maximisation of power and maximisation of efficiency as objectives with design stresses as the constraints. We have considered two different types of materials for this study. A new population-based evolutionary algorithm named selective breeding algorithm is considered to solve this design problem, with two different types of materials. In selective breeding algorithm, solutions are made to breed, mutate, sort and multiple better solutions are formed. Then fitness conditions are placed, best solutions are generated and results are compared with an existing design. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive procedure to calculate steady dynamic response and noise radiation generated by a gear reducer. In this process, the bearing force is obtained by solving the transmission system dynamic model, which is taken as the excitation of the gearbox, the noise generated by the gearbox vibration is researched by using FEM/BEM, so the dynamic characteristics of gear transmission and gearbox structure are demonstrated. Then the time history of node dynamic response and noise spectrum of the gearbox are obtained, and the effect of the excitation of frequency doubling of mesh frequency on the gearbox vibration and noise radiation is illustrated. The changes in radiated noise of gear reducers with load are calculated respectively by using both simulation and Kato's formulae, and results agree well with each other. The frequency range of the gearbox resonance is obtained by analyzing the spectral maps of the vibration and noise under the case of engine speed steadily increased. The study provides useful theoretical guideline to the design of the gearbox. [ABSTRACT FROM AUTHOR]

The cylindrical external gear reducers have a wide application in the industry. The aim of this paper is to facilitate the design and production of these machines by means of the application of the artificial intelligence technique known as case based reasoning to the design of their transmissions. The indexes for case retrieval were determined by means of expert criteria. The case base was built with gear reducer transmissions that were designed and manufactured in the industry. The case retrieval is carried out through the combination of a selection query and the nearest neighbor algorithm. By means of the integration of the case base with a CAD CAE system was obtained a case based design system of this kind of transmissions. The new design is carried out by adapting and checking the closest case to the current transmission. As the obtained solution is similar to an existing transmission, besides the design process, is also facilitated wheel production. [ABSTRACT FROM AUTHOR]

Abstract: In the condition monitoring of gear reducer, the labeled fault samples are sparse and expensive, while the unlabeled samples are plentiful and cheap. How to diagnose the faults occurring in complex and special gear reducer effectively becomes a troublesome problem in case of insufficient labeled samples or excess unlabeled samples. This paper presents a novel model for fault diagnosis based on empirical mode decomposition (EMD) and multi-class transductive support vector machine (TSVM), which is applied to diagnose the faults of the gear reducer. The experimental results obtain a very high diagnosis accuracy. Even though the number of unlabeled samples is 50 times as that of labeled samples, the mean of testing accuracy of the proposed novel method can reach at 91.62%, which distinctly precedes the testing success rates of the other similar models in the same experimental condition. [Copyright &y& Elsevier]

The paper mainly aims at the common problems existing in driving components, especially in harmonic reducer, RV reduction gear, etc. The paper put forward a new high performance filter transmission mechanism with high reliability, long life, large torque, small volume and lower weight, especially the innovation design method of high performance filter transmission mechanism. The new transmission mechanism is composed of eccentric sleeve, inner meshed planet gear pair of less teeth difference, filter spline pair, three ways thrust bearing and so on. Moreover, the paper has designed the eccentricity and tooth outline, made numerical simulation of the meshing process with pressure angle of 30, calculated the transmission efficiency, and checked the intensity of wheels. Furthermore, the paper has operated comprehensive tests about transmission precision, rigidity, backlash, and transmission efficiency. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a systematic approach for synthesizing novel configurations of totally integrated DC gear motors (DC motors and gear reducers). The topological structures, motion characteristics, and design constraints of existing DC gear motors are defined. And, the graph representations for each member and labels for magnetic circuits of integrated DC gear motors are defined. Desired configurations of DC motors integrated with a three-link and a four-link gear reducer are synthesized. And, one prototype of the three-link integrated BDC gear motor and another of the four-link integrated BLDC gear motors are presented. The results provide all possible basic configurations of the proposed novel integrated DC gear motors for further applications. [Copyright &y& Elsevier]